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Leveraging Molecular Mechanisms of Desorption to Enhance PFAS Bioavailability in Contaminated Soils
No full textPer- and polyfluoroalkyl substances (PFAS) are known for their strong binding properties to soil matrices owing to their amphiphilic properties. While most studies focus on the search for novel PFAS bio degraders, our knowledge of sustainable biomolecules that could drive PFAS desorption and make them more bioavailable is limited. This study investigated the effectiveness of rhamnolipids and organic acids in desorbing PFAS compounds from contaminated soil. Rhamnolipids (25 mg/L) significantly enhanced the release of PFAS compounds from soil, achieving up to 90 % desorption. Acetic acid provided 60–90 % desorption efficiency for most of the PFAS studied, except for PFDA and three sulfonic PFAS (L-PFBS, L-PFHxS, and L-PFOS), suggesting acid-induced charge modification and reduced sorption affinity. Increasing the concentration of either additive enhanced desorption across all PFAS types, with acetic acid achieving maximum desorption at 0.5 M. Desorption kinetics revealed that rhamnolipids and acetic acid accelerated the kinetics of slow-desorbing PFAS. While oxalic and malic acids hindered the desorption of carboxylic PFAS, they enhanced the desorption of sulfonates. Considering the higher sorption capacity of sulfonates to sediment and soil, with greater potential for bioconcentration, the sorption reversibility of oxalic and malic acids can be harnessed for enhanced remediation of sulfonic PFAS in situations where rhamnolipids and acetic acids are inapplicable. Our findings will potentially stimulate research aimed at identifying suitable biomolecule-producing plants and microbes for enhanced PFAS desorption. Leveraging eco-friendly molecular mechanisms of desorption will complement the activities of novel microbes and facilitate PFAS biodegradation and site reclamation
Scalable Transfer of DNA Origami–Directed Nanoparticle Arrays Onto Functional Surfaces using Thermal Release Tape
No full textPrecise integration of DNA origami nanostructures onto functional substrates is essential for advancing electronics, photonics, and biosensing. However, most surface-assembly methods are highly sensitive to substrate type, limiting their broader applications. Here, we introduce a thermal release tape (TRT)-assisted transfer method that enables efficient, large-area printing of DNA origami-guided gold nanoparticle (AuNP-DNA origami) arrays from mica onto diverse surfaces. The method relies on differential adhesion: AuNP-DNA origami arrays form strong electrostatic and hydrogen-bonding interactions with 3-aminopropyltriethoxysilane (APTES)-functionalized receiver substrates, while the TRT adhesive weakens upon heating to 120°C, allowing release from the tape. Using this approach, well-ordered AuNP-DNA origami arrays were transferred onto silicon, Silicon carbide, and glass coverslips. Under optimized conditions, on average, ∼70% of the prepatterned domain area on mica was successfully transferred to silicon while preserving the designed lattice geometry; non-optimized surface treatments or peeling conditions yielded lower transfer efficiencies, underscoring the need for process optimization. The TRT-assisted process is simple, scalable, and material-independent, avoiding lithographic patterning or substrate-specific self-assembly. This proof-of-concept study establishes a versatile platform for expanding substrate compatibility in DNA nanotechnology and provides a foundation for the large-scale integration of nanoscale patterns into functional devices
Dual-functional Nanoparticle Formulations for Simultaneous Intraocular Pressure Reduction and Neuroprotection in Glaucoma: A Review
No full textGlaucoma is a leading cause of irreversible blindness, driven by elevated intraocular pressure (IOP), progressive retinal ganglion cell (RGC) loss, and optic nerve degeneration. Current therapies rely on lowering IOP, which slows but does not halt disease progression. Dual-functional nanoparticle (NP) formulations represent a promising approach to simultaneously address these therapeutic targets. By improving ocular drug penetration, sustaining release, and enabling co-delivery of diverse agents, nanocarriers can achieve prolonged IOP reduction while directly preserving RGC and optic nerve against excitotoxicity, oxidative stress, inflammation, etc. In this work, we reviewed the glaucoma pathophysiology and the rationale for dual therapy. We then discussed major classes of NP systems and strategies that can fulfill dual-function therapy. The preclinical studies and early clinical developments were also highlighted. We also discussed the challenges of formulation stability, safety, and regulatory approval, and outlined future directions. Together, these advances position dual-functional NP systems as a transformative strategy for disease-modifying glaucoma therapy, bridging the gap between IOP control and neuroprotection to preserve vision
On-device Artificial Intelligence Solutions with Applications to Smart Environments
No full textRecent advances in Artificial Intelligence (AI) and the increasing availability of computational power have accelerated the diffusion of Intelligent Cyber-Physical Systems (ICPSs), enabling smart applications with reasoning capabilities. However, the limited resources of embedded and Edge devices significantly constrain the complexity of deep learning models that can be effectively deployed. Traditional approaches rely on cloud-based training and edge-only inference, a paradigm that becomes inadequate when low latency, privacy, security, and high customization are required. In this context, On-device AI is emerging as a new paradigm in which both training and inference are performed directly on the device, avoiding data transfer and enabling faster, more energy-efficient, and privacy-preserving intelligent systems. Despite the challenges associated with resource constraints, the benefits of this approach motivate the exploration of novel architectures, frameworks, and methodologies. Additionally, as edge devices increasingly handle sensitive data, security and privacy considerations become fundamental aspects of system design. In this special issue we invited high-quality research on On-device AI solutions for Smart Environments and Industry 4.0 applications. Overall, 20 papers were accepted covering a wide range of areas
Revisiting Ulam Stability for Boundary Value Problems
No full textThe main goal of this paper is to apply Ulam stability theory to boundary value problems for dynamic equations, while addressing several common misconceptions found in the existing literature. We identify the key issues that arise when applying Ulam stability to such problems and propose three distinct approaches to overcome them. To enhance clarity and accessibility, we begin with nonlinear ordinary differential equations and subsequently extend the analysis to nonlinear dynamic equations on time scales. Since a time scale is defined as any nonempty closed subset of the real numbers, our results are applicable to dynamic equations on continuous, discrete, or hybrid time domains
What is Engineering, Anyway?
No full textDr. David Bayless helps unpack what engineers actually do and why the field is so important. From different engineering disciplines to research on bioreactors designed for the moon, the conversation explores how engineers tackle big challenges — and how Missouri S&T is leading the way.https://scholarsmine.mst.edu/eee/1000/thumbnail.jp
Sustainable Pathways For Fish Waste Oil Valorization Into Biofuel: Process Synthesis And Case Study
No full textBiodiesel is a promising, sustainable alternative to fossil fuels such as petrol and diesel. Currently, biodiesel can be produced from edible plant oils and non-edible sources and wastes. Notably, fish waste oil is a sustainable resource for transesterification reactions to produce biodiesel. This research proposes a general process design methodology to investigate the potential of biodiesel production from fish waste oil as a pathway for waste-to-energy. The methodology integrates Pinch Analysis principles and process simulation to optimize the energy efficiency of a process design. Real data are collected on fish waste from fish industries in Egypt, focusing on three regions in northern Egypt with a total capacity of 7.5 tons per day (t/d). The research methodology is applied to the design of a biodiesel production plant with a fish waste oil capacity of 547.5 tons/year. The production process involves a transesterification reaction using methanol and NaOH as catalysts. The annual expected yields are 495.2 tons of biodiesel and 51.4 tons of glycerol. The base design indicates total heating and cooling energies of 6889.6 kW and 11,470.1 kW, respectively, and CO2 emissions of 19,343 tons/year. An improved design using Pinch Analysis achieves substantial energy savings of 47% in heating, 69% in cooling, and, 9202 tons of CO2 cut. The novelty of the work lies in developing and applying an integrated process design and energy minimization methodology. The work provides a transferable methodology that can be applied to other wastes
SADQN-Based Residual Energy-Aware Beamforming for LoRa-Enabled RF Energy Harvesting for Disaster-Tolerant Underground Mining Networks
No full textThe end-to-end efficiency of radio-frequency (RF)-powered wireless communication networks (WPCNs) in post-disaster underground mine environments can be enhanced through adaptive beamforming. The primary challenges in such scenarios include (i) identifying the most energy-constrained nodes, i.e., nodes with the lowest residual energy to prevent the loss of tracking and localization functionality; (ii) avoiding reliance on the computationally intensive channel state information (CSI) acquisition process; and (iii) ensuring long-range RF wireless power transfer (LoRa-RFWPT). To address these issues, this paper introduces an adaptive and safety-aware deep reinforcement learning (DRL) framework for energy beamforming in LoRa-enabled underground disaster networks. Specifically, we develop a Safe Adaptive Deep Q-Network (SADQN) that incorporates residual energy awareness to enhance energy harvesting under mobility, while also formulating a SADQN approach with dual-variable updates to mitigate constraint violations associated with fairness, minimum energy thresholds, duty cycle, and uplink utilization. A mathematical model is proposed to capture the dynamics of post-disaster underground mine environments, and the problem is formulated as a constrained Markov decision process (CMDP). To address the inherent NP hardness of this constrained reinforcement learning (CRL) formulation, we employ a Lagrangian relaxation technique to reduce complexity and derive near-optimal solutions. Comprehensive simulation results demonstrate that SADQN significantly outperforms all baseline algorithms: increasing cumulative harvested energy by approximately 11% versus DQN, 15% versus Safe-DQN, and 40% versus PSO, and achieving substantial gains over random beamforming and non-beamforming approaches. The proposed SADQN framework maintains fairness indices above 0.90, converges 27% faster than Safe-DQN and 43% faster than standard DQN in terms of episodes, and demonstrates superior stability, with 33% lower performance variance than Safe-DQN and 66% lower than DQN after convergence, making it particularly suitable for safety-critical underground mining disaster scenarios where reliable energy delivery and operational stability are paramount
The Impact of Value Homophily, Rational and Emotional Persuasion on Information Passing of Social Media Advertisements: A Model Comparison Approach
No full textIncreasingly, businesses collaborate with influencers and content creators (the source) to advertise on social media, with social media users being exposed to an environment saturated with unsolicited advertisements. With social contacts remaining the most trusted advertisement sources, users\u27 passing of such advertisements helps their dissemination and creates a specific kind of electronic word of mouth called information passing. Information passing involves users forwarding advertisements about products/services to someone else. Prior studies have found at least three factors influence information passing, including value homophily (similarity with the source as perceived by users), rational appeal (information about how a product can meet users\u27 utilitarian needs and increase personal gains), and emotional appeal (information about a product\u27s esthetic, pleasurable, and hedonic benefits to stir up positive feelings). There is some debate as to how these three factors interact to influence information passing. We propose, compare, and test three models. The direct model posits all three as standalone factors influencing users\u27 advertisement passing. The moderation model postulates value homophily interacts with rational and emotional appeal. The mediation model posits value homophily is caused by rational and emotional appeal. Our model comparison was performed using data collected from 412 social media users randomly exposed to a uniquely manipulated advertisement with an integrated survey via email. Our results demonstrate that although the direct and moderation model are significant, the direct model is a more parsimonious framework and overall, better for explaining and predicting users\u27 information passing without incurring unnecessary complexity. Theoretical and practical implications are discussed
Energetics Analysis of Solitary Waves using a Multi-layer Model
No full textThis study investigates the performance of a vertically-Lagrangian multi-layer model on numerically simulating shoaling and breaking two-dimensional solitary waves during both the breaking and post-breaking processes. The energy dissipation of the breaking event for the multi-layer waves is analyzed and compared to prior direct numerical simulation work with the same bathymetric and wave cases. It shows very similar data collapse to shallow-water inertial theory. For post-breaking behavior, bore characteristics are compared to an experimental study of bores formed from breaking solitary waves and similar results are found. While the multi-layer method was not found to behave sufficiently well for direct force measurement at a vertical wall, the resulting bore characteristic behavior is found to be sufficient for use in theoretical estimations of the impact force on the wall. These findings in this study suggest that vertically-Lagrangian multilayer models resolve propagating bores sufficiently well when trying to estimate dynamic loads on vertical seawalls with minimal model tuning